Layer-based defect detection using normalized sensor data

ABSTRACT

The disclosed embodiments relate to the monitoring and control of additive manufacturing. In particular, a method is shown for removing errors inherent in thermal measurement equipment so that the presence of errors in a product build operation can be identified and acted upon with greater precision. Instead of monitoring a grid of discrete locations on the build plane with a temperature sensor, the intensity, duration and in some cases position of each scan is recorded in order to characterize one or more build operations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 USC 119(e) to U.S. ProvisionalPatent Application No. 62/311,318, filed on Mar. 21, 2016, and entitled“LAYER-BASED DEFECT DETECTION USING NORMALIZED OFF-AXIS SENSOR DATA,”the disclosure of which is hereby incorporated by reference in itsentirety and for all purposes.

BACKGROUND OF THE INVENTION

Additive manufacturing, or the sequential assembly or construction of apart through the combination of material addition and applied energy,takes on many forms and currently exists in many specificimplementations and embodiments. Additive manufacturing can be carriedout by using any of a number of various processes that involve theformation of a three dimensional part of virtually any shape. Thevarious processes have in common the sintering, curing or melting ofliquid, powdered or granular raw material, layer by layer usingultraviolet light, high powered laser, or electron beam, respectively.Unfortunately, established processes for determining a quality of aresulting part manufactured in this way are limited. Conventionalquality assurance testing generally involves destruction of the part.While destructive testing is an accepted way of validating a part'squality, as it allows for close scrutiny of various internal portions ofthe part, such tests cannot for obvious reasons be applied to aproduction part. Consequently, ways of non-destructively verifying theintegrity of a part produced by additive manufacturing is desired.

One particular problem with characterizing the quality of the resultingpart is that data collected by a wide area thermal sensor offset from anadditive manufacturing build plane can be artificially biased by avarying distance between the thermal sensor and different portions ofthe build plane.

SUMMARY OF THE INVENTION

Embodiments of the present invention are related to a large subcategoryof additive manufacturing, which involves using an energy source thattakes the form of a moving region of intense thermal energy. In theevent that this thermal energy causes physical melting of the addedmaterial, then these processes are known broadly as welding processes.In welding processes, the material, which is incrementally andsequentially added, is melted by the energy source in a manner similarto a fusion weld.

When the added material takes the form of layers of powder, after eachincremental layer of powder material is sequentially added to the partbeing constructed, the heat source melts the incrementally added powderby welding regions of the powder layer creating a moving molten region,hereinafter referred to as the weld pool, so that upon solidificationthey become part of the previously sequentially added and melted andsolidified layers below the new layer that includes the part beingconstructed. As additive machining processes can be lengthy and includeany number of passes of the weld pool, it can be difficult to avoid atleast slight variations in the size and temperature of the weld pool asthe weld pool is used to solidify the part. It should be noted thatadditive manufacturing processes are typically driven by one or moreprocessors associated with a computer numerical control (CNC) due to thehigh rates of travel of the heating element and complex patterns neededto form a three dimensional structure.

In addition to applying to additive manufacturing operations, thedescribe method and apparatus can also be relevant to identifying andcharacterizing defects in laser marking operations.

An additive manufacturing method is disclosed that includes thefollowing: monitoring a heat source scanning across a powder bed usingan optical temperature sensor; scanning across different portions of thepowder bed with the heat source to produce a metal part; recording theintensity and duration of scans made by the heat source; generating acharacteristic curve from the optical temperature sensor for one or moreregions of the metal part using the recorded scan duration and intensitydata; comparing the characteristic curve of each region with a baselinecharacteristic curve associated with the respective region; anddetermining one of the regions is defective when the comparing shows adifference between the characteristic curve of the region and thebaseline characteristic curve that exceeds a predetermined threshold.

A manufacturing method is disclosed that includes the following:identifying one or more regions within a part where defects are morelikely to occur during the manufacturing method; recording sensor datafrom laser scans made within the identified one or more regions using anoptical temperature sensor; generating a characteristic curve for eachof the one or more regions using the sensor data collected for each ofthe recorded laser scans; comparing the characteristic curves tocorresponding a baseline characteristic curves; and determining one ormore of the regions is defective when the comparing shows a differencebetween the characteristic curve of the region and the baselinecharacteristic curve that exceeds a predetermined threshold.

Another additive manufacturing method is disclosed that includes thefollowing: creating a metal part on a powder bed using a scanning laser;recording sensor data for scans made by the laser in select regions ofthe metal part using an optical temperature sensor; determiningintensity and duration of each of the recorded scans; creating acharacteristic curve for each of the regions of the metal part based onthe intensity and duration of each scan associated with the region;comparing each of the characteristic curves to a baseline characteristiccurve associated with each of the regions; and determining based on thecomparing whether any of the regions are likely to have manufacturingdefects.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be readily understood by the following detaileddescription in conjunction with the accompanying drawings, wherein likereference numerals designate like structural elements, and in which:

FIG. 1 shows an exemplary additive manufacturing configuration suitablefor use with the described embodiments;

FIG. 2A shows a simplified perspective view of a thermal sensorpositioned with respect to a surface of a build plane;

FIG. 2B shows a top view of the build plane depicted in FIG. 2A and howdifferent portions of the build plane can be subject to laser scans ofvarying length and orientation′

FIG. 3 shows a graph plotting the intensity and duration of multiplesequential laser scans of different duration;

FIGS. 4A-4F show how data points collected by a thermal sensor can becorrected to remove variations caused by differences in scan length anddistance to generate a characteristic curve indicative of intensityvariation as a function of distance;

FIGS. 5A-5C each shows a graph representing experimental data collectedfor a layer and normalized in accordance with the description providedin conjunction with FIGS. 4A-4F;

FIG. 6 shows a flowchart describing a method of operations suitable foruse with the described embodiments;

FIG. 7A shows an exemplary time based photodiode (PD) signal for a givenscan length;

FIG. 7B shows a pyrometer signal used to indicate peak temperature,heating rate and cooling rate;

FIG. 8A shows an exemplary characteristic curve for a layer of a part;

FIG. 8B shows a characteristic curve normalized with true temperaturevalues;

FIG. 9 shows a representation of two different laser scans oriented inopposing directions;

FIG. 10 shows a flow chart representing a method for removing geometriceffects from thermal data collected by a thermal sensor such as aphotodiode; and

FIGS. 11A-11D show perspective and cross-sectional views of exemplaryparts and scan patterns associated with particular features of theexemplary parts.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Representative applications of methods and apparatus according to thepresent application are described in this section. These examples arebeing provided solely to add context and aid in the understanding of thedescribed embodiments. It will thus be apparent to one skilled in theart that the described embodiments may be practiced without some or allof these specific details. In other instances, well known process stepshave not been described in detail in order to avoid unnecessarilyobscuring the described embodiments. Other applications are possible,such that the following examples should not be taken as limiting.

In the following detailed description, references are made to theaccompanying drawings, which form a part of the description and in whichare shown, by way of illustration, specific embodiments in accordancewith the described embodiments. Although these embodiments are describedin sufficient detail to enable one skilled in the art to practice thedescribed embodiments, it is understood that these examples are notlimiting; such that other embodiments may be used, and changes may bemade without departing from the spirit and scope of the describedembodiments.

Photodiode (PD) sensors can be configured to measure the intensity ofradiated heat. While a PD sensor can be designed with a wide field ofview that has the advantage of being able to detect heat being emittedfrom anywhere across a large area, the PD sensor is not generally ableto determine from which portion of the field of view the radiated heatoriginated. Additionally, the amount of radiated heat detected by the PDsensor is reduced commensurate with the distance the PD sensor is fromthe source of the heat.

The inability of the PD sensor to be able to distinguish positioncoupled with the distance related variations in the detected signalsmakes carrying out any reliable temperature characterization of a movingheat source problematic without the benefit of additional data sources.For example, if a PD sensor had two portions of an object within itsfield of view that were sequentially heated to the same temperature buta first portion of the object was substantially closer to the PD sensorthan a second portion, the readings of the PD sensor would indicate thatthe temperature reached by the first portion was higher than thetemperature reached by the second portion. Consequently, any substantialgeometric variations in the areas in the field of view of the PD sensorcan contain inherent errors.

One solution to this problem is to add one or more additional sensorsconfigured to track the location of the heat source so that the distanceof the heat source from the PD sensor can be accounted for. For example,an imaging sensor could be configured to track the position of the heatsource. In cases where the heat source travels at high speeds, a highframe rate imaging sensor could be required to effectively track theheat source. By syncing the position of the heat source with respect tothe PD sensor with the temperature data provided by the PD sensor, thePD sensor data could be corrected for variations caused by distance.

The aforementioned PD sensor can be used to monitor radiated heat in anadditive manufacturing operation. In some additive manufacturingoperations the heat source can take the form of a laser scanning rapidlyacross a powder bed. This type of moving heat source is problematic fora PD sensor to monitor on account of the aforementioned intensityvariations caused by the heat source operating at varying distances fromthe PD sensor. In addition to the distance problems other systematicsources of error include errors generated by varying scan lengths of thelaser and varying directions of travel of the laser. Longer scans tendto raise the temperature of the powder bed more than shorter scans,causing longer laser scans to appear to reach higher temperatures thanshorter scan lengths. The direction of travel of the laser can also havean effect on detected intensity on account of powder accumulating infront of and obscuring some of the heat being generated by the laserfrom the PD sensor.

One way to overcome the systematic errors inherent with using a PDsensor to monitor the temperature of a powder bed during an additivemanufacturing operation is to create a baseline set of data correctedfor scan length and distance variations that can then be used to confirmthe quality of other layers. While the scan length variation isgenerally the largest source of intensity variation, unlike the distancevariations it can be characterized by the PD sensor. This is because theheat introduced by the laser in an additive manufacturing operation issubstantially hotter than any other source of heat on the build plane,so the PD sensor can be configured to monitor laser scan start and stoptimes to determine a duration for each detected laser scan. Thelength/duration of each scan can then be stored and associated with acorresponding scan.

The baseline can be created by: (1) normalizing the PD signal intensitydata by plotting PD signal intensity vs scan length for one or morelayers of an additive manufacturing operation; (2) making a best fitline through the raw PD signal intensity data; (3) applying a transformto the data that flattens the best fit line thereby normalizing the rawdata to account for scan length variation; (4) separating the normalizedPD signal intensity data into multiple bins, where each bin has scans ofsimilar length; (4) ranking the scans in each bin by intensity toproduce a curve indicative of the amount of variation due to distance;and (5) averaging the curves generated from each bin together togenerate a baseline characteristic curve. Ideally, when monitoringproduction of the layer associated with the baseline characteristiccurve some additional checks should be performed to confirm satisfactoryperformance of the additive manufacturing process while generating thelayer. These process checks could include destructive testing of theresulting part. The threshold at which a part can be considered to bedefective can vary based on the type of part, the material being usedwith the part and various other factors. The newly created baselinecharacteristic curve can be valid for use during the production of awide variety of parts as long as the powder properties, laser scanspeed, scan pattern and power are kept consistent.

The baseline characteristic curve can then be compared with curvescreated while producing production parts. In the same way that thebaseline characteristic curve is produced, a characteristic curve can beproduced for each layer of a production part. The baselinecharacteristic curve can then be compared with the characteristic curvesassociated with each layer and then any characteristic curves which aretoo different from the baseline characteristic curve can be flagged aspotentially containing a defect.

In some embodiments, the characteristic curves can be calibrated by anarrow field of view sensor configured to identify performance relatedparameters within the narrow field of view, such as for example, peaktemperature, heating rate and cooling rate. These performance relatedparameters can then be correlated with the characteristic curves to helpquantify the otherwise uncalibrated characteristic curve comparison.

These and other embodiments are discussed below with reference to FIGS.1-10; however, those skilled in the art will readily appreciate that thedetailed description given herein with respect to these figures is forexplanatory purposes only and should not be construed as limiting.

FIG. 1 shows an exemplary additive manufacturing system 100 suitable foruse with the described embodiments. An intense heat source 102 is inthis specific instance taken to be a laser. The beam 104 emitted by heatsource 102 originates at the laser head and passes through a partiallyreflective optic 106. This optic 106 is designed to be essentially fullytransmissive at the specific wavelength that the laser operates, andreflective at other optical wavelengths. Generally the laser wavelengthwill be infrared or near-infrared, or typically wavelengths of 1000 nmor greater. The laser can include a scanning head 108 that consists of xand y positioning galvanometers as well as a focus lens, such as anf-theta lens. The beam 104 is therefore focused and strikes powderdistributed across build plane 110 at a given location 111 thusgenerating a molten region of liquefied powder at location 111. Themolten region of liquefied powder emits optical radiation 112isotropically and uniformly over a large solid angle. Some of thisoptical radiation 112 will make its way back through the scanning head108 and is reflected by the partially reflective optic 106.

This reflected optical beam 114 then makes its way through one or moreanalytical instruments. As depicted, mirror 116 sends the reflectedoptical beam 114 to photodiode 118. In some embodiments mirror 116 canbe only partially reflective, allowing it to act as a beam splitter thatsends a portion of reflected optical beam 114 to one or more othersensors. Photodiode 118 can be capable of sensing a range of frequenciesat a high enough speed and recording rate to detect possible anomaliesoccurring during an additive manufacturing process, i.e. suddendepartures from an average or mean intensity level. Because photodiode118 has a relatively low resolution it can be configured to record dataat extremely high frame rates, so that photodiode 218 is capable ofdetecting very transient temperature excursions occurring during a buildprocess.

In addition to the aforementioned Lagrangian reference frame photodiode118, another aspect of the depicted sensor system is the existence of atleast one or more sensors configured to collect measurements made in anEulerian reference frame and are completely independent of theLagrangian reference frame. These Eulerian measurements can be used forcorrelation, calibration and characterization purposes. For example inFIG. 1, a stationary pyrometer 120 in the Eulerian reference frameindependently measures the temperature of a small region of build plane110 and can therefore provide a calibration to the measurements made bythe Lagrangian photodiode 118. The field of view 122 of the stationaryEulerian pyrometer 120 is suitably chosen so that it matches thecharacteristic dimension of the molten zone existing on build plane 110and made by the focused laser beam 104 at the specific location 111 towhich the scanning head 108 displaced and focused the beam 104.

FIG. 1 also shows thermal sensor 124 having a field of view 126, whichcan be configured to detect temperature changes in substantially anyportion of the top surface of build plane 110. In some embodiments,thermal sensor 124 can take the form of a photodiode and pyrometer 120can be configured to provide calibration information to Eulerianphotodiode 124, thereby allowing the voltages generated by Eulerianphotodiode 124 to be converted to temperatures that accuratelydistinguish the temperature of any point on the top surface of a partbeing formed on build plane 110.

FIG. 2A shows a simplified perspective view of thermal sensor 124positioned with respect to an entire surface of build plane 110. Thermalsensor 124 is offset from build plane 110 so that the distance betweenthermal sensor 124 and various portions of build plane 110 variessubstantially. An x-y coordinate system is also depicted showing howpositions across build plane 110 can be measured.

FIG. 2B shows a top view of build plane 110 and how a scan strategy fora given part can employ scan patterns with scan lines oriented indifferent directions and having different lengths. This results indifferent regions of build plane 110 being subject to laser scans ofvarying length and orientation. In this depiction, laser scan patternsA1 and A2 have substantially the same scan length and laser scans B1 andB2 have substantially the same scan length. Because the duration of thescans has a substantial impact upon the amount of energy reflected intophotodiode 124, calibration steps can be more accurately performed bycomparing laser scans of similar length. Scan patterns A1 and A2 wouldlikely be grouped together, but since scan pattern A1 is substantiallyfarther than scan pattern A2 is from thermal sensor 124, a detectedintensity of scans associated with scan pattern A1 can be substantiallyless than a detected intensity of scans associated with scan pattern A2.Scan pattern B1 and B2 could also be grouped together by scan length butare also substantially different distances from sensor 124. However, inaddition to being farther away, scan pattern B1 also has a differentorientation than scan pattern B2. While the difference in orientation ofthe scan patterns will generally cause variations in individual scanintensities, these differences can generally be averaged out when thescan patterns are arranged in alternating, opposite directions, asdepicted. For this reason, differences in intensity caused by scanorientation can be largely overlooked when looking at the scans as agroup.

FIG. 3 shows a graph plotting the intensity and duration of multiplesequential laser scans of different duration. Power output of the laseris maintained at the same setting for each of the laser scans. The graphshows how the intensity of light emitted for any given laser variessubstantially as a function of scan length. It should be noted thatafter a particular scan duration, intensity increases more slowly as theheat of the portions of the powder bed surrounding the melt pool (themelt pool represents that portion of the powder on the powder bed thatis liquefied as the laser scans across the powder bed) reaches a steadystate. As depicted, short scan 202 reaches a substantially smallerintensity than medium length scan 204, while longer length scan 206attains only a marginally higher maximum intensity than medium lengthscan 204.

A. Characteristic Curve Creation and Comparison

FIGS. 4A-4F show how data points collected by a thermal sensor can becorrected to remove variations caused by differences in scan length anddistance to generate a characteristic curve indicative of intensityvariation as a function of distance. FIG. 4A shows a graph representingall the scans detected while building one layer of a part. Trend line402 shows the average intensity for each scan length and how thedetected intensity increases rapidly when the laser or heat source firstcontacts the powder bed and then increases more slowly as the signalintensity approaches an asymptotic limit, which is not exceeded by thetrend line. The trend line can be represented by the following equation,where y′_(PD) is the fitted photodiode signal, y_(PD) is the rawintensity data and x_(SL) is the scan length:

y′ _(PD) =f(x _(SL))  Eq(1)

FIG. 4A also depicts a number of variance bars 404 that indicate therange of values for a particular scan length or narrow range of scanlengths. In some embodiments, the range can omit a top and bottomportion of the range, such as for example, the top and bottom 5% to omitany extreme outlying data points, which could skew the interpretation ofthe representation. The variation of the data from trend line 402 can beattributed to variations caused by certain laser scans being differentdistances from a thermal sensor measuring the laser scans. Somevariations can also be due to the laser scans being aligned in differentdirections. Different directions and distances of the laser scan withrespect to the thermal sensor can both result in varying amounts ofradiation reaching the thermal sensor.

FIG. 4B shows a graph representing a normalized version of the datadepicted in FIG. 4A. Instead of representing the raw sensor intensity asin FIG. 4A, the graph now represents signal intensity variation aboveand below a selected value. In this case, the asymptotic limit is chosenas the value to which the average intensity of each average scan lengthwill be set. The graph can be normalized by using the followingequation, where y_(AL) is the asymptotic limit:

y* _(PD) =y _(PD) −y′ ^(PD) +y _(AL)  Eq(2)

In this way, the intensity variations of each scan length can bedirectly comparable. While there are obvious differences in some scanlengths, in general the intensity variation is very similar. In somecases, such as for example represented by variance bars 404-1 and 404-2the range is substantially different. These differences can be due toscans of a particular length being more tightly grouped. For example,when a majority of the scans of a particular duration are localized inone portion of the build plane, the variation in intensity becomessubstantially less than scan lengths that are more widely spread acrossthe build plane on account of there being less distance variation whenthe scans are more localized.

FIG. 4C shows data points for a narrow range of scan lengths x_(i) tox_(i+1) that can correspond to one of variance bars 404 depicted inFIGS. 4A and 4B. Each one of these narrow ranges can be classified intoits own bin. A size of the range can vary based on the total number ofdata points, size of the build plane and other factors. In general, eachof these bins can have the same range.

FIG. 4D shows the data points of an exemplary bin ordered by intensity.By ordering the points in this manner, the resulting curve representsthe intensity change across the length of the build plane. While asingle bin provides one representation of intensity variation withrespect to distance, it should be appreciated that depending on thedistribution of the scans a single bin could provide a misleadingrepresentation. Consequently, a combination of curves from each binprovides a better overall representation of distance variation effect onintensity. However, since each bin doesn't necessarily contain the samenumber of points, the rank numbers of each bin can be normalized to adesired value, such as for example 1.

FIG. 4E shows a number of bin curves arranged on the same graph,represented by scan lengths L1, L2, L3 and L4. It should be noted thatbecause the highest intensities are assumed to be closest to the sensor,the x-axis actually decreases in distance from the sensor. Furthermore,the x-axis values are normalized so that for each bin the lowestintensity value corresponds to maximum distance and the highestintensity value corresponds to minimum distance. In FIG. 4F the trendlines or data points are all averaged together to produce acharacteristic curve for the layer associated with the scans.

FIGS. 5A-5B each shows a graph representing experimental data collectedfor a layer and normalized in accordance with the description providedin conjunction with FIGS. 4A-4F. Consequently, trend line 502 of FIG. 5Ais equivalent to the characteristic curve depicted in FIG. 4F. FIG. 5,however, also shows the data points 504 making up each of the bincurves, that get averaged together to define trend line 502.

FIG. 5B shows a similar graph in which scan speed of the laser whilemaking the layer was increased by about 10-15%. As can be seen thecurves while not entirely different do have a somewhat different shapeand point data point distribution. When trend line 502 represents aknown good additive manufacturing operation the difference in curvatureand signal intensity variation between trend lines 502 and 506 clearlyshows the presence of a systematic problem with the layer of the partassociated with the data in FIG. 5B.

FIG. 5C shows the two trend lines and data superimposed upon oneanother. The black data points correspond to trend line 502 and the graydata points correspond to trend line 506. In order to clearly comparethe intensity variation the intensity variation values for trend line506 and its associated data points have been lowered to coincide withtrend line 502. In this way it can be clearly seen that there issubstantially less variation in intensity for trend line 502 than thereis for trend line 506. This lower intensity variation can be due to thehigher scan speed resulting in shorter duration scans, which reduces theoverall amount of heat input into the product build. With the lower heatinput the thermal sensor ends up seeing a lower amount of variation whenviewing the build plane.

FIG. 6 shows a flowchart describing a method of operations suitable foruse with the described embodiments. At 602, a baseline characteristiccurve is compared to a test case characteristic curve. This comparisoncan take many forms including but not limited a qualitative comparisonin which the curves are overlaid. Alternatively, a more quantitativeanalysis can be performed in which a parametric best-fit is curve isgenerated to closely follow the characteristic curve. The parametricequation can then be utilized to create a feature vector that could beused in a Mahalanobis Distance Analysis Other statistical tests couldalso be used to compare the characteristic curves by comparing means andvariances associated with the curves. In each case, a threshold valuecan be assigned to determine what constitutes the two curves not beingconsidered to be the same. A determination that the characteristiccurves are not the same can generate an indication to an operator of theadditive machining tools that an off-nominal condition exists for thelayer associated with the test case. At 604, when the curves are deemedto be the same another test can be carried out to determine whetherthere are too many outliers for the layer. Data from a Lagrangiansensor, such as photodiode sensor 118 as depicted in FIG. 1, can beutilized to detect the outliers. Outliers can take the form of rapidtemperature excursions at the melt pool that can be caused byunintentional fluctuations in power, and variations in powder material.In some embodiments, these outlier data points can actually be removedfrom the characteristic curve analysis so that the characteristic curvesmore closely represent the steady state conditions of the additivemanufacturing process. Either way, when there are too many outlyingLagrangian data points or one data point is so high that the structuralintegrity of the layer is likely to be compromised, the layer can betagged as amounting to an off-nominal condition. When there are fewoutlier data points the layer of the part can be determined to beacceptable.

It should be noted that while the examples pertain primarily toaccounting for distance of the laser to the scan, direction of the scansand orientation of the scan that other data can be extrapolated from therecorded laser scan data. For example, when the scan strategy is knownfactors such as power variation, and laser speed among other factorscould be harvested from the collected intensity and duration dataprovided by the PD signal. In some embodiments, the scan strategy can beprovided by a controller associated with the laser. The scan strategycan provide position data for each of the scans made across the buildplane. In some embodiments, this position data can be associated withintensity and duration characteristics of each scan recorded during themanufacturing operation.

B. Correlation Between PD Signals and Pyrometer Signals

FIG. 7A shows an exemplary time based photodiode (PD) signal for a givenscan length. In general, for shorted segments there could be a rise inthe signal intensity with time because there may not be sufficient timeto reach a thermal quasi-steady state condition. The dotted linerepresents the average PD signal. This is the input to the PD signalprocess method discussed earlier.

It is seen that in general there is a rise, and plateau, and a fall tothe PD time based signal. Generally speaking, this corresponds to theheating rate, the (average) peak temperature during the scan, and thecooling rate. The problem is that the PD data is not calibrated at alland therefore it is difficult to assign physically relevant units tothese qualitative quantities.

FIG. 7B shows a pyrometer signal collected by a sensor such as pyrometer120 as depicted in FIG. 1. A comprehensive calibration method isdiscussed for using pyrometer signals to determine variouscharacteristics of an additive manufacturing operation in U.S.application Ser. No. 14/945,247, which is entitled “Multi-Sensor QualityInference and Control for Additive Manufacturing Processes”, which isincorporated herein by reference in its entirety and for all purposes.Generally, the pyrometer data provides higher resolution calibratedtemperature data that allows for the identification of features in thetemperature curve that help to determine true peak temperature, heatingand cooling rate information. However, it should be noted that in somecases photodiode data can also be used to characterize true peaktemperature, cooling rate and heating rate.

FIG. 8A shows an exemplary characteristic curve for a layer of a part.If we look at the PD signal corresponding to corrected temperaturecurves from the pyrometer in the region of the pyrometer field of view,and if we process the PD data from the witness region using the sameprocess used to generate the nominal PD characteristic curve for a givenlayer, then the PD data from corresponding to the pyrometer field ofview falls at some point along the characteristic curve. As depicted inFIG. 8A, the PD sensor is relatively close to the pyrometer field ofview, but there could be other regions in the part which are closer andtherefore have a higher PD signal intensity. The point where thepyrometer data intersects the trend line for the PD characteristic curvecan be viewed as a specific calibration point for this layer. It isreasonable to assign the peak temperature value to this point. However,the PD Characteristic curve should first be flattened out to some meanvalue to cancel out the effect of distance away from the sensor. Thesame data transformation would be applied to the “cloud” of points aswell as the 5% and 95% percentiles.

This results in the following transformed data being generated that isdepicted in FIG. 8B. The point shown in FIG. 8B above is theintersection point between the PD data from the witness region and thecharacteristic curve. Now it is a reasonable approximation to set theaverage PD value in the characteristic PD curve equal to the averagepeak temperature as measured by all the scans that went through thepyrometer field of view for that layer. The experimental 5% and 95%limit values are therefore also transformed into temperature upper andlower control limits naturally. Now when two characteristic curves arecompared actual temperature values can be compared instead of relativesensor voltages. In some embodiments, a characteristic curve such as theone depicted in FIG. 8B could be compared with another flattenedcharacteristic curve, and when trend lines fall within the 5 and 95percentile boundaries the trend lines could be considered to be nominal.Stricter or looser thresholds could also be applied. It should be notedthat while peak temperature is being applied in these examples, thatpyrometric data can also be used to determine heating rate and/orcooling rate of different portions of the build plane.

FIG. 9 shows a representation of two different laser scans oriented inopposing directions. Laser scans 902 and 904 can both be in roughly thesame location and have the same duration but be oriented in opposingdirections. Here it can be seen that laser scan 902 is consistentlyslightly higher than laser scan 904. This variation can be based uponpowder collecting in front of the laser beam and blocking some of theintensity of the laser beam. One reason the scan direction has not beenfactored out is because when scan patterns have laser scans arranged inalternating directions, any variation due to direction gets averaged outwhen considering the characteristic curve as a whole. In embodimentswhere the scan pattern were more randomized, additional correctionscould be implemented to clear up any variations due to scan direction.For example, a high-speed camera could be used to report on a directionof each scan and so that scan direction could be correlated withreadings taken by a thermal sensor/photodiode.

FIG. 10 shows a flow chart representing a method for removing geometriceffects from thermal data collected by a thermal sensor such as aphotodiode. At 1002, plot signal intensity vs scan length for every scanmaking up one layer of a part. At 1004, make a best-fit curve throughthe plot. At 1006, flatten the best-fit curve by applying a correctionfactor, so that the intensity variation for each different scan lengthis about equivalent. At 1008, organize the data from the signalintensity vs scan length into bins of similar scan length. At 1010, rankorder and plot each data point from each bin by intensity. At 1012,normalize the x-axis of each plot so the plot of each bin is equivalentregardless of the number of data points. At 1014, superimpose the rankedscan length based curve. At 1016, put a best-fit line through thiscompilation of curves to generate a characteristic curve.

It should be appreciated that the specific steps illustrated in FIG. 10provide a particular method of for removing geometric effects fromthermal data collected by a thermal sensor according to an embodiment ofthe present invention. Other sequences of steps may also be performedaccording to alternative embodiments. For example, alternativeembodiments of the present invention may perform the steps outlinedabove in a different order. Moreover, the individual steps illustratedin FIG. 10 may include multiple sub-steps that may be performed invarious sequences as appropriate to the individual step. Furthermore,additional steps may be added or removed depending on the particularapplications. One of ordinary skill in the art would recognize manyvariations, modifications, and alternatives.

Identification of Regions of Interest

FIG. 11A shows a perspective view of an exemplary part 1100 suitable formanufacture by an additive manufacturing process. Part 1100 can includea series of protrusions 1102 arranged along a wall 1104 of part 1100. Insome embodiments, protrusions 1102 can take the form of cooling finsconfigured to convectively dissipate heat from part 1100. FIG. 11B showsa cross-sectional view of a portion of part 1100 in accordance withsection line A-A. A diagonal pattern of alternating direction scan lines1106 are depicted. The pattern of scan lines 1106 illustrate howparticular scan lines 1106 near a geometric feature such as tip region1108 can be substantially shorter than the other scan lines used tobuild part 1100. This can also occur when generating parts with narrowwalls that limit the effective scan length. For example, a series ofchannels extending through part 1100 could also create situations inwhich the scan pattern included particularly short scan lines 1106.

As previously shown in FIG. 3, these short scan lines can result in tipregions 1108 of protrusions 1102 not reaching a high enough temperatureto properly form protrusions 1102 when scan lines 1106 fall below athreshold length at which a steady state temperature is achieved. Insome embodiments, the amount of power delivered by the laser to tipregion 1108 can be increased in order to deliver enough energy to avoida situation in which tip region 1108 includes defects formed on accountof a lower amount of energy being delivered than desired. Othersolutions could include slowing the scan speed across smaller featuresof the part. Due to the shorter length of each scan line 1106 withinprotrusion 1102, it may be desirable to develop profiles that identifydesirable scan profiles for scan lines 1106 that form each tip region1108. By identifying the scan lines 1106 that form each of tip regions1108, profiles can be determined for each tip region 1108. It should beappreciated that scan lines 1106 associated with a particular geometricfeature can span multiple layers of the part.

In some embodiments, performance of the operation within a particularregion of interest can be tested empirically by doing destructivetesting on a batch of different parts. In this way, instead of having acharacteristic curve for the entire part, characteristic curves can bedeveloped for regions of interest within part 1100. By applying thecurve to smaller regions of part 1100, small performance variations canbecome more evident. For example, when 95% of the scan lines associatedwith building the part are within normal operating parameters but the 5%outside of the normal operating parameters are localized within certainregions of the part, a characteristic curve incorporating all the scanline data could mask the presence of the 5% of measurements localized ina particular area of the part. In particular, when a threshold value isestablished at 25% off nominal and only 5% of the scans are flagged asbeing off-nominal, increasing the number of off-nominal scans by afactor of 5 times only increases the metric from 6.5% to 11%. Thisrepresents a substantial variation which can be difficult to detect whenconsidering the part as a whole. Therefore dividing the part intoregions as described above avoids this dilution effect.

Some factors that can be considered when identifying regions of interestwithin a part include at least the following: empirical evidence showinghigher incidences of defects in a particular region; regions of highmetallurgical cooling rates, either determined by physics based models,rules of thumb or prior experience; a 100% experiential rule which takesprior history and posterior probabilities of defect occurrence intoaccount; and rules based on design guidelines provided by human experts.

Defects within the part can be caused by any one or more of thefollowing parameters being outside nominal operating parameters: powderparticle size distribution; particle composition and oxidation state;powder recycle state, i.e. state of reused powders; powder spreadingmethod and consistency of this method; shielding gas composition, flowrate, and flow pattern (laminar vs. turbulent); laser power; laser focalcharacteristics; laser scan speed; laser scan head optical, mechanical,and opto-mechanical characteristics; programming variations andpotential problems; scan pattern and scan strategy on a given layer; andscan strategy as it changes with geometry. Given the large number ofpotential sources of error, associating multiple characteristic curveswith particular features of a part can be quite helpful in identifyingand then recognizing the cause of any problem that arises.

FIG. 11C shows a perspective view of an exemplary part 1150 suitable formanufacture by an additive manufacturing process. Part 1150 can includean oval shaped wall 1152 extending around a periphery of part 1150. FIG.11D shows a cross-sectional top view of a portion of part 1150 inaccordance with section line B-B. In particular, when the scan strategyresults in scan lines 1106 being oriented as depicted, distal end region1158 of part 1150 can narrow down to an extent that laser parametersneed to be adjusted to deliver a sufficient amount of heat to distal endregion 1158 when the laser scans along scan lines 1106 within distal endregion 1158. Other features likely to increase off-nominal behaviorinclude sharp corners, overhangs, etc. In some embodiments, regions oflow risk can also be identified. For example, during normal operationsevery scan within a higher-risk area could be record, while areas withinmedium risk areas could be periodically sampled and areas within lowrisk areas could be ignored.

It should be appreciated that the described methods based oncharacterizing an additive manufacturing operation using recorded laserscans could also be applied in other industries. For example, in a lasermarking operation separate regions could be associated with each letter,number or indicia associated with a particular laser marking operation.Depending on complexity each letter could be more or less closelymonitored for defects.

The various aspects, embodiments, implementations or features of thedescribed embodiments can be used separately or in any combination.Various aspects of the described embodiments can be implemented bysoftware, hardware or a combination of hardware and software. Thedescribed embodiments can also be embodied as computer readable code ona computer readable medium for controlling manufacturing operations oras computer readable code on a computer readable medium for controllinga manufacturing line. The computer readable medium is any data storagedevice that can store data, which can thereafter be read by a computersystem. Examples of the computer readable medium include read-onlymemory, random-access memory, CD-ROMs, HDDs, DVDs, magnetic tape, andoptical data storage devices. The computer readable medium can also bedistributed over network-coupled computer systems so that the computerreadable code is stored and executed in a distributed fashion.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the describedembodiments. However, it will be apparent to one skilled in the art thatthe specific details are not required in order to practice the describedembodiments. Thus, the foregoing descriptions of specific embodimentsare presented for purposes of illustration and description. They are notintended to be exhaustive or to limit the described embodiments to theprecise forms disclosed. It will be apparent to one of ordinary skill inthe art that many modifications and variations are possible in view ofthe above teachings.

What is claimed is:
 1. An additive manufacturing method, comprising:monitoring a heat source scanning across a powder bed using an opticaltemperature sensor; scanning across different portions of the powder bedwith the heat source to produce a metal part; recording the intensityand duration of scans made by the heat source; generating acharacteristic curve from the optical temperature sensor for one or moreregions of the metal part using the recorded scan duration and intensitydata; comparing the characteristic curve of each region with a baselinecharacteristic curve associated with the respective region; anddetermining one of the regions is defective when the comparing shows adifference between the characteristic curve of the region and thebaseline characteristic curve that exceeds a predetermined threshold. 2.The additive manufacturing method of claim 1, further comprisingadjusting one or more characteristics of the heat source in accordancewith the difference between the characteristic curve of the region andthe baseline characteristic curve for the region when the layer isdetermined to be defective.
 3. The additive manufacturing method ofclaim 1, wherein generating the characteristic curve comprisesapproximating the distance of each scan from the optical temperaturesensor by ranking a set of similar duration scans by intensity.
 4. Theadditive manufacturing method of claim 3, wherein the temperature sensoris an off-axis temperature sensor configured to gather Eulerian datafrom the powder bed.
 5. The additive manufacturing method of claim 1,wherein recording the intensity duration of scans further comprisesrecording the position of each of the recorded scans.
 6. The additivemanufacturing method of claim 1, wherein a substantial portion of thefield of view of the optical temperature sensor is filled by the meltpool.
 7. The additive manufacturing method of claim 1, wherein the heatsource is a laser.
 8. The additive manufacturing method of claim 7,wherein the optical temperature sensor shares optics with the laser. 9.The additive manufacturing method of claim 1, wherein the baselinecharacteristic curve is determined at least in part by empirical data.10. The additive manufacturing method of claim 1, further comprising:identifying one or more regions of the part more likely to besusceptible to defects; and recording only those scans of the laseroccurring within the identified regions of the part.
 11. A manufacturingmethod, comprising: identifying one or more regions within a part wheredefects are more likely to occur during the manufacturing method;recording sensor data from laser scans made within the identified one ormore regions using an optical temperature sensor; generating acharacteristic curve for each of the one or more regions using thesensor data collected for each of the recorded laser scans; comparingthe characteristic curves to corresponding a baseline characteristiccurves; and determining one or more of the regions is defective when thecomparing shows a difference between the characteristic curve of theregion and the baseline characteristic curve that exceeds apredetermined threshold.
 12. The manufacturing method of claim 11,further comprising identifying areas within the part where defects areless likely to occur.
 13. The manufacturing method of claim 12, whereinrecording laser scans made within the identified one or more regionscomprises recording laser scans only within the one or more regionsusing the temperature sensor.
 14. The manufacturing method of claim 11,wherein the manufacturing method is an additive manufacturing method.15. The manufacturing method of claim 11, wherein the manufacturingmethod is a laser-based marking operation.
 16. An additive manufacturingmethod, comprising: creating a metal part on a powder bed using ascanning laser; recording sensor data for scans made by the laser inselect regions of the metal part using an optical temperature sensor;determining intensity and duration of each of the recorded scans;creating a characteristic curve for each of the regions of the metalpart based on the intensity and duration of each scan associated withthe region; comparing each of the characteristic curves to a baselinecharacteristic curve associated with each of the regions; anddetermining based on the comparing whether any of the regions are likelyto have manufacturing defects.
 17. The additive manufacturing method ofclaim 16, wherein the select regions correspond to one or more geometricfeatures of the metal part.
 18. The additive manufacturing method ofclaim 17, wherein the geometric features are selected from the groupconsisting of a sharp corner, a protrusion and an overhang.
 19. Theadditive manufacturing method of claim 16, further comprisingassociating position information with the intensity and duration data ofeach scan.
 20. The additive manufacturing method of claim 19, whereinthe position information is received from a controller associated withthe scanning laser.